Iterative approach for parametric PSF estimation

In this paper a parametric point spread function (PSF) estimation method is presented. This method can be employed for estimating the parameters of linear motion blur vector. The proposed method works on single image and estimates angle and length of motion blur vector to generate required PSF for deblurring. This method is based on step-by-step estimation of motion blur vector. In the first approximation the blind method considers short length vectors in all directions and deblurs image with PSF of these candidate vectors. The quality of deblurred image is assessed by a no-reference quality measurement metric which is proposed in this paper. The proposed no-reference image quality metric evaluates degradation in sharpness of edges and the amount of resulted artifact caused by saturated pixels, in the deblurred image. Those motion vectors that caused unacceptable deblurring results are omitted in the next iteration. The approximation is improved by increasing the length of the remaining vectors and the same process continues iteratively. The process goes on until only one vector remains as the estimation for motion blur vector. Experimental results show that in estimation of motion blur vectors, the length estimation error is less than one pixel in 85% of the cases and angle estimation error in 95% of the cases is less than one degree. Comparing with a conventional method indicates that the proposed method shows more than four times improvement in length estimation and 10% improvement in angle estimation. Moreover, the proposed method has comparatively lower computational load than other conventional deblurring methods.